PopularMajor Project
Artificial Intelligence
Face Recognition Attendance System
Camera-based attendance with liveness detection and analytics
- Python
- OpenCV
- FaceNet
- Flask
- +2
₹4,900₹5,999
4.8/301 reviews
Artificial Intelligence · Major Project
Object Detection with YOLO & TensorFlow
General object detection plus training on your own classes
2-3 weeks build
Overview
Starts with a COCO-pretrained YOLOv8 detecting 80 everyday classes, then walks through the full custom-training loop: labelling your own images, training, validating and exporting. A TensorFlow Lite export runs the custom model outside PyTorch, and a Streamlit app handles images, video and webcam with adjustable confidence and class filters.
What makes it stand up
- 01COCO-pretrained detection on images, video and webcam
- 02Labelling guide and scripts for a custom dataset
- 03Transfer learning to new classes with validation curves
- 04TensorFlow Lite export for lightweight inference
- 05mAP, precision-recall curves and a confusion matrix
Module breakdown
- 01
Pretrained inference
YOLOv8 on the 80 COCO classes.
- 02
Labelling
Annotation workflow and YOLO format conversion.
- 03
Custom training
Fine-tuning, augmentation and early stopping.
- 04
Export
ONNX and TensorFlow Lite conversion and checks.
- 05
App
Streamlit detector with thresholds and class filters.
After this, you will be able to
- Explain anchors, IoU and non-maximum suppression
- Defend your custom dataset and its evaluation
- Demo detection of a class you trained yourself
Technology stack
- Python
- YOLOv8
- TensorFlow
- OpenCV
- Streamlit
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 2-3 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support